\section{Related Work}
\label{sec:related}
This paper is mainly related to two areas of research: 1) trajectory prediction of object; 2) task allocation in MCS.
\subsection{Trajectory Prediction of Object}
Observing the importance of trajectory prediction
(e.g., narrow the tracking scope) in object tracking,
much effort has been made to explore it
\cite{YamaguchiBOB11,HorvitzK12,
KhezerlouZTLL21,XuWL16}.
From the perspective of data used,
the literature of trajectory prediction problem can be classified into two categories:
individual oriented and general oriented.
Several previous works on the destination prediction problem
made use of personal trip data.
For example,~\cite{YamaguchiBOB11} proposed an approach
that uses personal and social factors to
estimate pedestrian destination and social relationships.
\cite{HorvitzK12} utilized trip durations to
build Bayesian model, which serves to limit geographic
extent of candidate destinations.
Some other works
\cite{KhezerlouZTLL21,XuWL16}, including ours,
uses anonymous crowd trip data
that contains no traveler information.

Most existing trajectory prediction methods
focus on probabilistic models,
in which the historical trajectories are used to
train various Markov Chain (MC) and the \textit{top-k} most probable
destinations are returned.
One typical approach is to
partition the map into the grid cells
\cite{JingGWLLY18},
or roads into segments~\cite{chen2019crowdtracking},
and trip is decomposed into
a sequence of transitions between cells or segments
to build the model.
Another point-cluster-based MC prediction algorithm
was proposed in~\cite{ashbrook2002learning},
in which GPS data was automatically clustered
into meaningful locations and then used
as the states of the Markov process.
\cite{XuWL16} introduced a new
data-driven nonprobabilistic framework,
which directly operates on the trajectories
and makes the prediction.

In this paper, we adopt a probabilistic N-Gram model \cite{damashek1995gauging},
which calculates the next probable movement of the object
by all previous positions, with cluster-level granularity,
to learn semantic information (e.g., home, school and intersection).

\subsection{Task Assignment in MCS}
Task assignment becomes a critical issue
in MCS-powered tracking systems
when trajectory prediction phase
returns an arrival region, e.g., a grid, a road segment,
\textit{top-k} clusters etc,
which requires many workers to
finish the tracking task collectively.
Many researches have been made
in this MCS-based applications
\cite{song2020coverage,li2019prediction,xu2020unified,qian2021optimal}.
For example,
Guo et al. investigated the multitask-oriented worker selection problem
for large-scale MCS platforms,
with the objective of optimizing task allocation
under the situation of the time-sensitive tasks and delay-tolerant tasks,
respectively in~\cite{guo2016activecrowd}.
Yucel et al. introduced a task assignment strategy 
in~\cite{yucel2021coverage},
with the consideration of each user's preference
and reliability.
The prediction-based task allocation algorithm (PBTA)
was proposed in~\cite{li2019prediction},
with the objective of deriving the maximum overall system utility.
Most of these existing effort focuses on
the offline task assignment,
which selects workers for tasks with given location contexts.
while~\cite{XiaoWHCW17} introduced an online task assignment
algorithm to solve makespan sensitive problems.
What's more,
\cite{xu2020unified} proposed
a two-stage resource allocation process,
including an  phase and another sequential online phase.


Intuitively,
object tracking can be viewed as online task,
the sensing location of which changes over time.
Therefore,
our task assignment strategy applies an online model.
